<\/p>\n\n\n\n
It\u2019s the graduation season again, and it\u2019s about time for the graduation defense this year. Graduation design projects this year involved eye movement research, \u201cheads-down tribe\u201d on campus, human-computer interface interaction design, and queuing theory application research. Except for queuing theory application research, which was selected by one of the students independently, other topics were given for reference this year. Among them, the human-computer interface interaction design subject was the first attempt to combine with the subject of the major of Information Management and Information System. Two students worked on the same project from different perspectives (Back-end database development, front-end interactive interface design). The specific projects are as follows:<\/p>\n\n\n\n
Eye movement research project \u2013 eye movement research on visual contrast and intent affecting the attention of advertising keywords<\/p>\n\n\n\n
Research project on \u201cheads-down tribe\u201d on campus\u2014\u2014discomfort measurement on upper limb musculoskeletal system of college students with different levels of mobile phone use, research on influencing factors and prevention strategies of neck and shoulder pain of \u201cheads-down tribe\u201d on campus<\/p>\n\n\n\n
Human-computer interface interaction design project\u2014\u2014human-computer interface interaction design of c2c second-hand book information system on campus<\/p>\n\n\n\n
Application research project of queuing theory\u2014\u2014parking space matching and management charging standard of shopping mall underground parking lot based on queuing theory model<\/p>\n\n\n\n
The research project of \u201cheads-down tribe\u201d on campus is my main research direction in recent years. There were three related topics here. Different research methods were used to research and design the discomfort of the upper limb musculoskeletal system of \u201cheads-down tribe\u201d on campus. Survey tools such as questionnaire and Likert scale were used for several times. Obviously, questionnaire and scale data are not continuous variables, and parametric statistical methods cannot be used directly. Even sEMG data and eye movement data collected during the ergonomic experiment needed to be tested for normality before using the parametric test method. Therefore, it is necessary to make a generalization of correlation analysis statistical methods and nonparametric statistical methods for different data types.<\/p>\n\n\n\n
1. Data types of variables<\/strong><\/p>\n\n\n\n The most common data classification method is to divide data according to the measurement level of data. Data can be divided into categorical variables, ordinal variables, equidistant variables and ratio variables. Equidistant and ratio variables are continuous variables, and categorical and ordinal variables are discrete variables. Equidistant variables have equal units but no absolute zero point, and can perform addition and subtraction operations, while cannot perform multiplication and division operations. Ratio variables have both equal units and absolute zero points, and can perform four arithmetic operations. Likert scale data are ordinal variables. Questionnaire data and independent variables in the experimental design are mostly categorical variables, and sEMG data and eye movement data are ratio variables. For ordinal variables such as Likert scale data, if they are identified as interval variables by the Mantel-Haenszel trend test, you can analyze interval ordinal variables as continuous variables.<\/p>\n\n\n\n 2. Correlation analysis of variables with different data types<\/strong><\/p>\n\n\n\n Pearson correlation is used to analyze the strength of linear association between two continuous variables, and the population from which the two columns of variables come must be normally or approximately normally distributed.<\/p>\n\n\n\n For correlation analysis between two ordinal variables, Spearman correlation is generally used to test the strength and direction of association with at least one ordinal variable, or two continuous variables but the population from which they are derived is not normal distribution or distribution is unknown.<\/p>\n\n\n\n Kendall\u2019s tau-b correlation is a nonparametric analysis method used to test the strength and direction of association with at least one ordinal variable.<\/p>\n\n\n\n For the correlation analysis between two categorical variables, Chi-square test can be used to test their independence. This test can only analyze the statistical significance of the correlation and cannot reflect the strength of the association. It is often combined with Cramer\u2019s V test to indicate the strength of the association.<\/p>\n\n\n\n For the correlation analysis between an ordinal variable and a continuous variable, the continuous variable is first tested as an ordinal variable, that is, to analyze the relationship between the two ordinal variables. Spearman correlation can be used.<\/p>\n\n\n\n For a detailed description of this part, please refer to the document as follows.<\/p>\n\n\n\n \u8981\u505a\u76f8\u5173\u6027\u5206\u6790\uff0c\u8be5\u5982\u4f55\u9009\u62e9\u6b63\u786e\u7684\u7edf\u8ba1\u65b9\u6cd5\uff1f<\/a><\/p>\n\n\n\n 3. Normality test of sample data<\/strong><\/p>\n\n\n\n One-sample K-S test can check whether the sample comes from a normally distributed population. Binomial method can test whether the actual distribution of the data in the binomial distribution conforms to a certain hypothesis, expectation, or specific form.<\/p>\n\n\n\n 4. Nonparametric statistics of variables with different data types<\/strong><\/p>\n\n\n\n Nonparametric tests with large samples are more reliable. In the case of a single sample, Chi-square test can be used to test the degree of cooperation to analyze whether the actual frequency of the variable value is consistent with the theoretical frequency.<\/p>\n\n\n\n To test whether the two independent samples come from the same population, or whether the data distribution of the two samples is the same, for the data that cannot meet the normal distribution condition, or two ordinal variables, Mann-Whitney U test needs to be used, which corresponds to independent sample t-test in parametric statistical method. It requires the independent variable to be a categorical variable with two levels, and the dependent variable to be an ordinal variable or continuous variable with at least an ordinal scale.<\/p>\n\n\n\n To test the significance between two related samples, it is usually applicable to two experimental design situations: repeated measures design and paired sample design. Four types of Wilcoxon signed-rank test, Sign test, McNemr test, and Marginal Homogeneity test can be used, corresponding to paired samples t-test and correlation coefficient significance test in parametric statistical method. Wilcoxon signed-rank test is the most widely used and is suitable for data with continuous distribution and symmetry. Sign test has a slightly lower statistical precision. McNemr test is only suitable for dichotomous correlated variables, and Marginal Homogeneity test is an extension of the McNemr test, which can test variables with multiple responses, but only for ordinal variables, and they are especially suitable for pretest-posttest experimental designs.<\/p>\n\n\n\n To test the significance among multiple independent samples, Kruskal-Wallis H test, Median test and Jonckheere-Terpstra test can be used, which correspond to the variance analysis of one-way completely randomized design in parametric statistical method. It requires the independent variable to be a categorical variable with more than two levels and the dependent variable be an ordinal variable or a continuous variable with at least an ordinal scale. Kruskal-Wallis H test corresponds directly to one-way ANOVA in parametric statistics and is frequently used. Median test is actually a contingency table analysis with low precision. Jonckheere-Terpstra test is similar to the Kruskal-Wallis H test, with higher precision when the grouping variable is ordinal.<\/p>\n\n\n\n To test the significance among multiple related samples, Friedman test, Cochrans Q test and Kendall W test can be used, which correspond to the variance analysis of randomized block design in parametric statistical method. Friedman test is an extension of Wilcoxon signed-rank test. Cochrans Q test is only applicable to several related dichotomous variables, which is an extension of McNemr test. Kendall W test is used to test whether the opinions of different evaluators are consistent. Both Friedman test and Cochrans Q test are applicable to repeated measures design and paired sample design. If there is a significant difference in the test results, further post-hoc tests are required, such as Wilcoxon signed-rank test.<\/p>\n\n\n\n For a detailed description of this part, please refer to the literature as follows.<\/p>\n\n\n\n \u4e01\u56fd\u76db, \u674e\u6d9b\u7f16\u8457. SPSS\u7edf\u8ba1\u6559\u7a0b\u2014\u2014\u4ece\u7814\u7a76\u8bbe\u8ba1\u5230\u6570\u636e\u5206\u6790. \u5317\u4eac: \u673a\u68b0\u5de5\u4e1a\u51fa\u7248\u793e, 2014.<\/p>\n\n\n\n \u53c2\u8003\u8bd1\u6587<\/strong><\/p>\n\n\n\n \u6bd5\u4e1a\u8bbe\u8ba1\u8bfe\u9898\u4e2d\u7684\u4e0d\u540c\u6570\u636e\u7c7b\u578b\u53d8\u91cf\u7684\u76f8\u5173\u4e0e\u975e\u53c2\u7edf\u8ba1<\/strong><\/p>\n\n\n\n \u5173\u952e\u8bcd<\/strong>\uff1a\u6bd5\u4e1a\u8bbe\u8ba1\uff0c\u6570\u636e\u7c7b\u578b\uff0c\u6b63\u6001\u68c0\u9a8c\uff0c\u975e\u53c2\u7edf\u8ba1<\/p>\n\n\n\n \u53c8\u5230\u4e00\u5e74\u6bd5\u4e1a\u5b63\uff0c\u9a6c\u4e0a\u5c31\u8981\u8fdb\u884c\u4eca\u5e74\u7684\u6bd5\u4e1a\u8bbe\u8ba1\u7b54\u8fa9\u4e86\u3002\u4eca\u5e74\u7684\u6bd5\u4e1a\u8bbe\u8ba1\u8bfe\u9898\u6d89\u53ca\u773c\u52a8\u7814\u7a76\u3001\u6821\u56ed\u4f4e\u5934\u65cf\u7814\u7a76\u3001\u4eba\u673a\u754c\u9762\u4ea4\u4e92\u8bbe\u8ba1\u548c\u6392\u961f\u8bba\u5e94\u7528\u7814\u7a76\uff0c\u9664\u6392\u961f\u8bba\u5e94\u7528\u7814\u7a76\u4e3a\u5b66\u751f\u81ea\u4e3b\u9009\u9898\u5916\uff0c\u5176\u4ed6\u8bfe\u9898\u5747\u4e3a\u4eca\u5e74\u7ed9\u5b9a\u7684\u53c2\u8003\u9009\u9898\u3002\u5176\u4e2d\u4eba\u673a\u754c\u9762\u4ea4\u4e92\u8bbe\u8ba1\u8bfe\u9898\u4e3a\u9996\u6b21\u5c1d\u8bd5\u4e0e\u4fe1\u606f\u7ba1\u7406\u4e0e\u4fe1\u606f\u7cfb\u7edf\u4e13\u4e1a\u4ea4\u53c9\u9009\u9898\uff0c\u8bfe\u9898\u7814\u7a76\u5bf9\u8c61\u9009\u81ea\u4fe1\u606f\u7ba1\u7406\u4e0e\u4fe1\u606f\u7cfb\u7edf\u4e13\u4e1a\u5b66\u751f\u6bd5\u4e1a\u8bbe\u8ba1\uff0c\u5373\u4e24\u4e2a\u4e13\u4e1a\u7684\u4e24\u540d\u5b66\u751f\u5206\u522b\u4ece\u4e0d\u540c\u89d2\u5ea6\uff08\u540e\u53f0\u6570\u636e\u5e93\u5f00\u53d1\u3001\u524d\u53f0\u4ea4\u4e92\u754c\u9762\u8bbe\u8ba1\uff09\u5bf9\u540c\u4e00\u4e3b\u9898\u5c55\u5f00\u8bbe\u8ba1\u3002\u5177\u4f53\u9009\u9898\u5982\u4e0b\uff1a<\/p>\n\n\n\n \u773c\u52a8\u7814\u7a76\u8bfe\u9898\u2014\u2014\u89c6\u89c9\u5bf9\u6bd4\u6027\u4e0e\u610f\u56fe\u5f71\u54cd\u5e7f\u544a\u5173\u952e\u5b57\u6ce8\u610f\u529b\u7684\u773c\u52a8\u7814\u7a76<\/p>\n\n\n\n \u6821\u56ed\u4f4e\u5934\u65cf\u7814\u7a76\u8bfe\u9898\u2014\u2014\u624b\u673a\u4e0d\u540c\u4f7f\u7528\u7a0b\u5ea6\u7684\u5927\u5b66\u751f\u4e0a\u80a2\u808c\u8089\u9aa8\u9abc\u7cfb\u7edf\u4e0d\u9002\u6d4b\u8bc4\u3001\u6821\u56ed\u4f4e\u5934\u65cf\u9888\u80a9\u75bc\u75db\u7684\u5f71\u54cd\u56e0\u7d20\u53ca\u9632\u8303\u7b56\u7565\u5206\u6790\u3001\u4e0d\u540c\u60c5\u5883\u4e0b\u6821\u56ed\u4f4e\u5934\u65cf\u624b\u673a\u4f7f\u7528\u7684\u8868\u9762\u808c\u7535\u7814\u7a76<\/p>\n\n\n\n \u4eba\u673a\u754c\u9762\u4ea4\u4e92\u8bbe\u8ba1\u8bfe\u9898\u2014\u2014\u9ad8\u6821c2c\u4e8c\u624b\u4e66\u4fe1\u606f\u7cfb\u7edf\u4eba\u673a\u754c\u9762\u4ea4\u4e92\u8bbe\u8ba1<\/p>\n\n\n\n \u6392\u961f\u8bba\u5e94\u7528\u7814\u7a76\u8bfe\u9898\u2014\u2014\u57fa\u4e8e\u6392\u961f\u8bba\u6a21\u578b\u7684\u5546\u573a\u5730\u4e0b\u505c\u8f66\u573a\u8f66\u4f4d\u5339\u914d\u53ca\u7ba1\u7406\u6536\u8d39\u6807\u51c6<\/p>\n\n\n\n \u6821\u56ed\u4f4e\u5934\u65cf\u7814\u7a76\u8bfe\u9898\u662f\u8fd1\u5e74\u6765\u672c\u4eba\u7684\u4e3b\u8981\u7814\u7a76\u65b9\u5411\uff0c\u8fd9\u91cc\u6709\u4e09\u4e2a\u76f8\u5173\u9009\u9898\uff0c\u8fd0\u7528\u4e86\u4e0d\u540c\u7684\u7814\u7a76\u65b9\u6cd5\u5bf9\u6821\u56ed\u4f4e\u5934\u65cf\u4e0a\u80a2\u808c\u8089\u9aa8\u9abc\u7cfb\u7edf\u4e0d\u9002\u5c55\u5f00\u7814\u7a76\u4e0e\u8bbe\u8ba1\uff0c\u5176\u4e2d\u591a\u6b21\u8fd0\u7528\u4e86\u95ee\u5377\u3001\u91cc\u514b\u7279\u91cf\u8868\u7b49\u8c03\u67e5\u5de5\u5177\u3002\u5f88\u663e\u7136\u95ee\u5377\u548c\u91cf\u8868\u6570\u636e\u90fd\u4e0d\u662f\u8fde\u7eed\u53d8\u91cf\uff0c\u90fd\u65e0\u6cd5\u76f4\u63a5\u4f7f\u7528\u53c2\u6570\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5373\u4fbf\u662f\u4eba\u56e0\u5b9e\u9a8c\u8fc7\u7a0b\u4e2d\u6536\u96c6\u7684\u8868\u9762\u808c\u7535\u6570\u636e\u548c\u773c\u52a8\u6570\u636e\uff0c\u5728\u8fd0\u7528\u53c2\u6570\u68c0\u9a8c\u65b9\u6cd5\u524d\u4e5f\u9700\u8981\u8fdb\u884c\u6b63\u6001\u68c0\u9a8c\u3002\u56e0\u6b64\uff0c\u6709\u5fc5\u8981\u5bf9\u4e0d\u540c\u6570\u636e\u7c7b\u578b\u7684\u76f8\u5173\u5206\u6790\u7edf\u8ba1\u65b9\u6cd5\u53ca\u975e\u53c2\u7edf\u8ba1\u65b9\u6cd5\u505a\u4e00\u6b21\u5f52\u7eb3\u3002<\/p>\n\n\n\n 1\u3001\u53d8\u91cf\u7684\u6570\u636e\u7c7b\u578b<\/strong><\/p>\n\n\n\n \u6700\u5e38\u89c1\u7684\u6570\u636e\u5206\u7c7b\u65b9\u6cd5\u662f\u6309\u7167\u6570\u636e\u7684\u6d4b\u91cf\u6c34\u5e73\u6765\u5212\u5206\uff0c\u53ef\u5c06\u6570\u636e\u533a\u5206\u4e3a\u5206\u7c7b\u53d8\u91cf\u3001\u987a\u5e8f\u53d8\u91cf\u3001\u7b49\u8ddd\u53d8\u91cf\u548c\u6bd4\u7387\u53d8\u91cf\uff0c\u5176\u4e2d\u7b49\u8ddd\u53d8\u91cf\u548c\u6bd4\u7387\u53d8\u91cf\u4e3a\u8fde\u7eed\u53d8\u91cf\uff0c\u5206\u7c7b\u53d8\u91cf\u548c\u987a\u5e8f\u53d8\u91cf\u4e3a\u79bb\u6563\u53d8\u91cf\u3002\u7b49\u8ddd\u53d8\u91cf\u6709\u76f8\u7b49\u5355\u4f4d\u4f46\u6ca1\u6709\u7edd\u5bf9\u96f6\u70b9\uff0c\u53ef\u8fdb\u884c\u52a0\u51cf\u8fd0\u7b97\uff0c\u4e0d\u80fd\u8fdb\u884c\u4e58\u9664\u8fd0\u7b97\uff1b\u6bd4\u7387\u53d8\u91cf\u65e2\u6709\u76f8\u7b49\u5355\u4f4d\u4e5f\u6709\u7edd\u5bf9\u96f6\u70b9\uff0c\u53ef\u4ee5\u8fdb\u884c\u56db\u5219\u8fd0\u7b97\u3002\u91cc\u514b\u7279\u91cf\u8868\u6570\u636e\u4e3a\u987a\u5e8f\u53d8\u91cf\uff0c\u95ee\u5377\u6570\u636e\u548c\u5b9e\u9a8c\u8bbe\u8ba1\u4e2d\u7684\u81ea\u53d8\u91cf\u5927\u90e8\u5206\u4e3a\u5206\u7c7b\u53d8\u91cf\uff0c\u8868\u9762\u808c\u7535\u6570\u636e\u548c\u773c\u52a8\u6570\u636e\u5747\u4e3a\u6bd4\u7387\u53d8\u91cf\u3002\u5bf9\u4e8e\u91cc\u514b\u7279\u91cf\u8868\u6570\u636e\u8fd9\u4e00\u7c7b\u7684\u987a\u5e8f\u53d8\u91cf\uff0c\u901a\u8fc7Mantel-Haenszel \u8d8b\u52bf\u68c0\u9a8c\uff08\u6839\u636e\u7814\u7a76\u8005\u5bf9\u987a\u5e8f\u53d8\u91cf\u7c7b\u522b\u7684\u8d4b\u503c\uff0c\u5224\u65ad\u4e24\u4e2a\u987a\u5e8f\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u8d8b\u52bf\uff09\u8ba4\u5b9a\u4e3a\u5b9a\u8ddd\u53d8\u91cf\u7684\u8bdd\uff0c\u4e5f\u53ef\u4ee5\u5c06\u5b9a\u8ddd\u987a\u5e8f\u53d8\u91cf\u4f5c\u4e3a\u8fde\u7eed\u53d8\u91cf\u8fdb\u884c\u5206\u6790\u3002<\/p>\n\n\n\n 2\u3001\u4e0d\u540c\u6570\u636e\u7c7b\u578b\u53d8\u91cf\u7684\u76f8\u5173\u5206\u6790<\/strong><\/p>\n\n\n\n Pearson\u76f8\u5173\u7528\u4e8e\u5206\u6790\u4e24\u4e2a\u8fde\u7eed\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u8054\u5f3a\u5ea6\uff0c\u4e24\u5217\u53d8\u91cf\u6240\u6765\u81ea\u7684\u603b\u4f53\u5fc5\u987b\u4e3a\u6b63\u6001\u6216\u8fd1\u4f3c\u6b63\u6001\u5206\u5e03\u3002<\/p>\n\n\n\n \u5bf9\u4e8e\u4e24\u4e2a\u987a\u5e8f\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u5206\u6790\uff0c\u4e00\u822c\u91c7\u7528Spearman\u76f8\u5173\uff08\u53c8\u79f0Spearman\u79e9\u76f8\u5173\uff09\uff0c\u7528\u4e8e\u68c0\u9a8c\u81f3\u5c11\u6709\u4e00\u4e2a\u987a\u5e8f\u53d8\u91cf\u7684\u5173\u8054\u5f3a\u5ea6\u548c\u65b9\u5411\uff0c\u6216\u8005\u4e24\u4e2a\u8fde\u7eed\u53d8\u91cf\u4f46\u6240\u6765\u81ea\u7684\u603b\u4f53\u975e\u6b63\u6001\u5206\u5e03\u6216\u5206\u5e03\u672a\u77e5\u3002<\/p>\n\n\n\n Kendall\u2019s tau-b\u76f8\u5173\u7528\u4e8e\u68c0\u9a8c\u81f3\u5c11\u6709\u4e00\u4e2a\u987a\u5e8f\u53d8\u91cf\u5173\u8054\u5f3a\u5ea6\u548c\u65b9\u5411\u7684\u975e\u53c2\u5206\u6790\u65b9\u6cd5\uff0c\u8be5\u68c0\u9a8c\u4e0eSpearman\u76f8\u5173\u7684\u5e94\u7528\u8303\u56f4\u57fa\u672c\u4e00\u81f4\uff0c\u4f46\u66f4\u9002\u7528\u4e8e\u5b58\u5728\u591a\u79cd\u5173\u8054\u7684\u6570\u636e\uff08\u5982\u5217\u8054\u8868\uff09\u3002<\/p>\n\n\n\n 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